Figures, Tables, and Exhibits xi Preface xxv The Authors xxix Part I Using Data for Improvement 1 Chapter 1 Improvement Methodology 3 Fundamental Questions for Improvement 4 What Are We Trying to Accomplish? 5 How Will We Know That a Change Is an Improvement? 6 What Changes Can We Make That Will Result in Improvement? 7 The PDSA Cycle for Improvement 8 Tools and Methods to Support the Model for Improvement 11 Analysis of Data from PDSA Cycles 18 Chapter 2 Using Data for Improvement 25 What Does the Concept of Data Mean? 25 How Are Data Used? 26 Types of Data 32 The Importance of Operational Definitions 37 Data for Different Types of Studies 40 Use of Sampling 42 What About Sample Size? 45 Stratification of Data 49 What About Risk or Case-Mix Adjustment? 51 Transforming Data 52 Analysis and Presentation of Data 58 Using a Family of Measures 61 Chapter 3 Understanding Variation Using Run Charts 67 Introduction 67 What Is a Run Chart? 67 Use of a Run Chart 68 Constructing a Run Chart 69 Examples of Run Charts for Improvement Projects 70 Probability-Based Tests to Aid in Interpreting Run Charts 76 Special Issues in Using Run Charts 85 Stratification with Run Charts 99 Using the Cumulative Sum Statistic with Run Charts 101 Chapter 4 Learning from Variation in Data 107 The Concept of Variation 107 Depicting Variation 110 Introduction to Shewhart Charts 113 Interpretation of a Shewhart Chart 116 Establishing and Revising Limits for Shewhart Charts 121 When Do We Revise Limits? 124 Stratification with Shewhart Charts 126 Rational Subgrouping 128 Shewhart Charts with Targets, Goals, or Other Specifications 131 Special Cause: Is It Good or Bad? 133 Other Tools for Learning from Variation 136 Chapter 5 Understanding Variation Using Shewhart Charts 149 Selecting the Type of Shewhart Chart 149 Shewhart Charts for Continuous Data 152 I Charts 152 Examples of Shewhart Charts for Individual Measurements 155 Rational Ordering with an Individual Chart 158 Effect of the Distribution of the Measurements 158 Example of Individual Chart for Deviations from a Target 159 X? and S Shewhart Charts 160 Shewhart Charts for Attribute Data 163 The P Chart for Classification Data 166 C and U Charts for Counts of Nonconformities 173 Process Capability 184 Process Capability from an I Chart 186 Capability of a Process from X? and S Chart (or R chart) 187 Capability of a Process from Attribute Control Charts 188 Capability from a P Chart 188 Capability from a C or U Chart 189 Appendix 5.1 Calculating Shewhart Limits 192 I Chart 192 X? and S Charts 193 X? and S Control Chart Calculation Form 195 P Chart 197 P Chart Calculation Form: Constant Subgroup Size 197 P Chart Calculation Form: Variable Subgroup Size 198 C Chart 199 U Chart 200 Chapter 6 Shewhart Chart Savvy: Dealing with Some Issues 201 Designing Effective Shewhart Charts 201 Tip 1: Type of Data and Subgroup Size 201 Tip 2: Rounding Data 202 Tip 3: Formatting Charts 202 Typical Problems with Software for Calculating Shewhart Charts 207 Characteristics to Consider When Purchasing SPC Software 211 Some Cautions When Using I Charts 211 Part II Advanced Theory and Methods with Data 217 Chapter 7 More Shewhart-Type Charts 219 Other Shewhart-Type Charts 220 NP Chart 221 X? and Range (R) Chart 221 Median Chart 224 Shewhart Charts for Rare Events 226 G Chart for Opportunities Between Rare Events 228 T Chart for Time Between Rare Events 229 Some Alternatives to Shewhart-Type Charts 231 Moving Average Chart 233 Cumulative Sum (CUSUM) Chart 236 Exponentially Weighted Moving Average (EWMA) 242 Standardized Shewhart Charts 244 Multivariate Shewhart-Type Charts 245 Chapter 8 Special Uses for Shewhart Charts 253 Shewhart Charts with a Changing Center Line 253 Shewhart Charts with a Sloping Center Line 253 Shewhart Charts with Seasonal Effects 255 Transformation of Data with Shewhart Charts 258 Shewhart Charts for Autocorrelated Data 264 Shewhart Charts for Attribute Data with Large Subgroup Sizes (Over-Dispersion) 269 Prime Charts (p' and U') 269 Comparison Charts 274 Confidence Intervals and Confidence Limits 275 Shewhart Charts for Case-Mix Adjustment 278 Chapter 9 Drilling Down into Aggregate Data for Improvement 281 What Are Aggregate Data? 281 What Is the Challenge Presented by Aggregate Data? 282 Introduction to the Drill Down Pathway 285 Stratification 287 Sequencing 288 Rational Subgrouping 288 An Illustration of the Drill Down Pathway: Adverse Drug Events (ADES) 289 Drill Down Pathway Step One 290 Drill Down Pathway Step Two 290 Drill Down Pathway Step Three 292 Drill Down Pathway Step Four 297 Drill Down Pathway Step Five 302 Drill Down Pathway Step Six 304 Part III Applications of Shewhart Charts in Health Care 307 Chapter 10 Learning from Individual Patient Data 309 Examples of Shewhart Charts for Individual Patients 310 Example 1: Temperature Readings for a Hospitalized Patient 311 Example 2: Bone Density for a Patient Diagnosed with Osteoporosis 313 Example 3: PSA Screening for Prostate Cancer 314 Example 4: Shewhart Charts for Continuous Monitoring of Patients 316 Example 5: Asthma Patient Use of Shewhart Charts 318 Example 6: Monitoring Weight 318 Example 7: Monitoring Blood Sugar Control for Patients with Diabetes 320 Example 8: Monitoring Patient Measures in the Hospital 321 Example 9: Using Shewhart Charts in Pain Management 322 Chapter 11 Learning from Patient Feedback to Improve Care 325 Patient Surveys 326 Summarizing Patient Feedback Data 329 Presentation of Patient Satisfaction Data 336 Using Patient Feedback for Improvement 337 The Plan-Do-Study-Act Cycles (PDSA) Cycle for Testing and Implementing Changes 338 Using Patient Satisfaction Data in Planning for Improvement 344 Special Issues with Patient Feedback Data 346 Are There Challenges When Summarizing and Using Patient Satisfaction Survey Data? 346 Does Survey Scale Matter? 347 Chapter 12 Using Shewhart Charts in Health Care Leadership 349 A Health Care Organization's Vector of Measures 349 Developing a Vector of Measures 350 Displaying and Learning from a Vector of Measures 351 "So How Do We Best Display a Vector of Measures?" 358 Administrative Issues with Vector of Measures 361 Some Examples of Other Vectors of Measures 362 Emergency Department: 363 Primary Care Center 364 Health Authority 364 Large Urban Hospital 366 Part IV Case Studies 369 Chapter 13 Case Studies Using Shewhart Charts 371 Case Study A: Improving Access to a Specialty Care Clinic 372 Case Study B: Radiology Improvement Projects 381 Case Study C: Reducing Post-CABG Infections 388 Case Study D: Drilling Down into Percentage of C-Sections 399 Case Study E: Accidental Puncture/Laceration Rate 409 Case Study F: Reducing Hospital Readmissions 418 Case Study G: Variation in Financial Data 425 Index 435 Shewhart Chart Selection Guide 446
Lloyd P. Provost is a cofounder of Associates in Process Improvement, the developers of the Model for Improvement roadmap and the Quality as a Business Strategy template for focusing organizations on improvement. Lloyd is a senior fellow at the Institute for Healthcare Improvement, where he supports the use of data for learning in programs. Sandra K. Murray is a principal in Corporate Transformation Concepts, an independent consulting firm. She is faculty for the Institute for Healthcare Improvement's year-long Improvement Advisor Professional Development Program and their Breakthrough Series College. Sandra has taught numerous programs through the National Association for Healthcare Quality. Her active cohort of client organizations encompasses the spectrum of health care delivery.